dask_expr._groupby.SeriesGroupBy.get_group
dask_expr._groupby.SeriesGroupBy.get_group¶
- SeriesGroupBy.get_group(key)¶
Construct DataFrame from group with provided name.
This docstring was copied from pandas.core.groupby.groupby.GroupBy.get_group.
Some inconsistencies with the Dask version may exist.
- Known inconsistencies:
If the group is not present, Dask will return an empty Series/DataFrame.
- Parameters
- nameobject (Not supported in Dask)
The name of the group to get as a DataFrame.
- objDataFrame, default None (Not supported in Dask)
The DataFrame to take the DataFrame out of. If it is None, the object groupby was called on will be used.
Deprecated since version 2.1.0: The obj is deprecated and will be removed in a future version. Do
df.iloc[gb.indices.get(name)]
instead ofgb.get_group(name, obj=df)
.
- Returns
- same type as obj
Examples
For SeriesGroupBy:
>>> lst = ['a', 'a', 'b'] >>> ser = pd.Series([1, 2, 3], index=lst) >>> ser a 1 a 2 b 3 dtype: int64 >>> ser.groupby(level=0).get_group("a") a 1 a 2 dtype: int64
For DataFrameGroupBy:
>>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame(data, columns=["a", "b", "c"], ... index=["owl", "toucan", "eagle"]) >>> df a b c owl 1 2 3 toucan 1 5 6 eagle 7 8 9 >>> df.groupby(by=["a"]).get_group((1,)) a b c owl 1 2 3 toucan 1 5 6
For Resampler:
>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex( ... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15'])) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 2023-02-15 4 dtype: int64 >>> ser.resample('MS').get_group('2023-01-01') 2023-01-01 1 2023-01-15 2 dtype: int64